Car Pooling
Abstract
Car pooling has emerged as a sustainable solution
to reduce traffic congestion, lower carbon
emissions, and save on transportation costs. This
paper explores various existing methods and
introduces a new approach to improve the
efficiency and user experience of car pooling
systems. With advancements in machine learning
and mobile technology, car pooling can be
optimized to provide a convenient, cost-effective,
and environmentally friendly alternative to singlepassenger
driving. By integrating real-time data,
user preferences, and dynamic routing algorithms,
our proposed method aims to enhance the matching
process and optimize travel routes.
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References
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